Cognitive RPA: The next level of business automation

The possibilities of Cognitive RPA go far beyond traditional automation. The integration of machine learning, natural language processing and machine vision opens up a new dimension in the processing of unstructured data. 

But what does this innovation mean for companies? 

In this blog article you will learn everything you need to know, from the basics of Cognitive RPA, its capabilities and how you can use Konfuzio for more efficient, intelligent business process automation.

cognitive rpa overview

Cognitive RPA at a glance

Cognitive RPA, a further development of Robotic Process Automation (RPA), uses artificial intelligence (AI) technologies such as optical character recognition (OCR), machine learning, natural language processing and machine visionto optimize the interactions between employees and customers within business processes.

This advanced form of RPA mimics human actions and integrates seamlessly with processes such as learning, reasoning and self-correction. Where traditional RPA relies on structured data, Cognitive RPA extends automation to unstructured data sources such as scanned documents, emails and voice recordings.

The true strength of cognitive automation is its ability to handle complex, less rule-based tasks. 

Unlike unsupervised RPA, Cognitive RPA can handle exceptions on its own, such as recognizing a date in the wrong format or filling out information in a form. This innovative technology is revolutionizing automation by enabling companies to respond more efficiently and flexibly to challenges without relying on human intervention.

RPA vs. cognitive automation

Robotic process automation (RPA) and cognitive automation are two different approaches to automating business processes, each using different technologies and capabilities.

Robotic Process Automation (RPA)

RPA refers to the automation of repetitive, rule-based tasks using software robots. These robots mimic the interactions of humans with digital systems by manipulating user interfaces, extracting data and making simple decisions. 

RPA is particularly suitable for tasks that have clear rules and structured data, and can automate repetitive processes in various business areas.

Cognitive Automation

Cognitive automation goes one step further and integrates artificial intelligence (AI) technologies into the automation process. This involves the use of advanced functions such as machine learning, natural language processing (NLP) and machine vision. 

In contrast to RPA, cognitive automation can also process unstructured data sources, handle complex tasks and recognize patterns in data. This form of automation enables greater adaptability to changing situations and can even learn independently to adapt to new scenarios.

Overall, it can be said that RPA is well suited to clearly defined, repetitive tasks, while cognitive automation is able to handle more complex and less structured tasks through the use of AI technologies, resulting in an extended automation capability.

cognitive rpa abilities

Cognitive RPA skills

Cognitive RPA (Robotic Process Automation) is characterized by advanced cognitive capabilities that differ from traditional RPA. These capabilities make it possible not only to automate repetitive tasks, but also to understand, learn and adapt to more complex scenarios.

Machine Learning (ML)

The ability to learn from experience and adapt to new situations enables a cognitive RPA system to generate personalized responses to customer queries in customer service, for example. 

Likewise, the automatic Improvement of work processes on the basis of acquired knowledge, as with the use of Cognitive RPA in the finance department, continuous Optimization of accounting processes ahead.

Natural Language Processing (NLP)

The ability to understand and interpret human-like language enables Cognitive RPA in a service desk to understand requests from users in natural language and automatically take relevant actions. 

NLP also enables interaction with users in natural language and the Processing unstructured text dataas is the case in market research, for example, when Cognitive RPA identifies trends and sentiments from customer reviews.

Machine vision

The ability to interpret visual information is used in the manufacturing industry, where a cognitive RPA performs visual inspections to detect production errors or deviations. 

In addition, machine vision enables the automated processing and analysis of images and videos, for example in the healthcare industry, where a cognitive RPA analyzes radiological images to identify anomalies or disease patterns.

Decision making

The ability to make independent decisions based on data and rules improves problem-solving skills and enables autonomous action in certain scenarios. 

One example of this is a cognitive RPA in the Supply Chain Management, which makes independent decisions about order quantities based on historical data.

Pattern recognition

The ability to identify patterns and trends in large data sets, for example, enables cognitive RPA to automatically detect deviations or anomalies in processes.

Contextual understanding

The ability to understand the context of information opens up the possibility of targeted action by taking relevant information into account and adapting actions accordingly.

Example:

Suppose a customer makes an inquiry via different channels, including email, chat and telephone. By applying cognitive automation, the system can understand the context of the interaction, regardless of the channel. It may recognize that the customer has already made a similar request via email. 

In this context, the system automatically extracts relevant information from the previous email and provides it to the support agent to ensure a consistent and efficient response.

Self-optimization

The ability to monitor, analyze and improve your own processes leads to continuous adaptation to changing environments and requirements, enabling you to increase efficiency and performance in business operations with the use of Cognitive RPA.

cognitive rpa optimize

Cognitive automation as an improvement to RPA

The integration of cognitive automation in Robotic Process Automation (RPA) opens up new dimensions and significantly improves the efficiency of the automation process. 

A decisive advantage lies in the optimization of data usage. By applying cognitive RPA, natural language processing and text analytics can be used to convert unstructured data, such as that found in documents and emails, into structured formats. This precise conversion enables an RPA system to use this data effectively in automated processes.

In addition, cognitive automation enables the integration of automated decision-making. With the help of predictive analyses, a robot can make independent decisions based on the situation at hand. The cognitive ability of machine learning enables the system to learn autonomously, expand its capabilities and continuously improve certain aspects of its functionality.

The strength of cognitive RPA therefore lies in its ability to go beyond the scope of structured data and also efficiently process unstructured data such as documents and emails. 

Advantages and challenges

Advantages of cognitive RPAChallenges of cognitive RPA
1. Efficiency improvement: Cognitive RPA enables faster and more precise execution of complex tasks.1. Complexity and implementation costs: The integration of cognitive capabilities often requires specialized expertise and can mean higher implementation costs.
2. Processing of unstructured data: The ability to understand natural language and visual information allows the processing of unstructured data sources.2. Data protection and compliance: The handling of sensitive data requires strict data protection measures in order to meet legal requirements.
3. Better decision making: Cognitive RPA can make independent decisions based on data and rules, leading to optimized business processes.3. Human-robot collaboration: The integration of cognitive systems often requires seamless collaboration between humans and robots, which may require cultural and organizational adjustments.
4. Flexibility and adaptability: The ability to continuously adapt to new information and changing requirements improves the flexibility of business processes.4. Lack of transparency: Cognitive systems can be opaque due to their complexity, which makes it difficult to understand and monitor decisions.
5. Innovative areas of application: Cognitive RPA opens up new areas of application in areas such as healthcare, customer service and analytics.5. Continuous further development: The rapid development of artificial intelligence requires constant adaptation and training of cognitive systems.
6. Improved customer interaction: The use of natural language processing improves interaction with customers and users.6. Acceptance and training: Employees may need to be trained to work effectively with the new cognitive systems and there may be resistance to automation.

Cognitive RPA & data protection

In the field of cognitive RPA (robotic process automation), there are specific aspects relating to data protection and compliance that need to be taken into account. Here are some important points:

Comply with data protection guidelines

Organizations should ensure that all cognitive RPA implementations comply with applicable data protection policies and laws. 

This includes, for example, compliance with the General Data Protection Regulation (DSGVO) in the European Union or comparable laws in other regions.

Identify and protect sensitive data

As cognitive RPA can also work with unstructured data, it is important to identify sensitive information and protect it appropriately. This applies in particular to personal data and confidential company information.

Data security in processing

The transmission and processing of data by cognitive RPA systems should be protected by appropriate security measures to prevent unauthorized access or data leaks.

Transparency and traceability

As cognitive algorithms are inherently complex, you need to make the processes in which you use the algorithms clear and transparent. This allows you to make decisions comprehensible and meet the requirements of data protection regulations.

Consent and notification

If cognitive RPA systems process personal data, you should obtain the consent of the data subjects and inform them of the purpose for which their data will be used.

Carry out a risk assessment

Companies carry out a comprehensive Risk assessment for their Cognitive RPA implementation to identify potential data protection and compliance risks and take appropriate measures to minimize risks.

Involve the data protection officer

It may be useful to involve a data protection officer in the implementation process of Cognitive RPA to ensure that Privacy policy are complied with and the implementation meets the highest standards.

Regular training for employees

Training for employees is particularly relevant to raise awareness of data protection and compliance requirements in connection with cognitive RPA. This will help you to minimize potential risks.

By paying attention to these aspects, companies can ensure that their Cognitive RPA implementation meets the highest data protection and compliance standards while taking advantage of this innovative technology.

use cases on background

Use Cases

Use case - omnichannel communication made easy

One exciting application for cognitive RPA is in the area of omnichannel communication. 

Today, customers interact with companies through various touchpoints and channels such as chat, interactive IVR, apps, messaging and more. 

The integration of RPA into these channels opens up the possibility of giving customers more autonomy without having to rely on the support of a human representative.

Thanks to cognitive RPA capabilities, the automated system not only understands the customer's intent, but also interprets unstructured data associated with the customer. It can predict the behavior and then execute corresponding requests in the backend. 

The combination of AI and cognitive automation enables you to monitor the entire customer journey and continuously integrate yourself into it.

A concrete example illustrates this: 

A chatbot in a bank that largely automates the process of opening an account. The customer can ask the chatbot for an online form, fill it out and upload Know Your Customer (KYC) documents. The form is then sent to a robot for initial processing, which performs a credit check, for example, and extracts data from the customer's driver's license or ID card using OCR.

Use case - Manual data extraction and processing in the finance department

Problem:

Employees in the finance department often have to extract and process large amounts of data manually, which is time-consuming and error-prone.

Solution: Cognitive RPA for automated data extraction and processing

A cognitive RPA system can extract data from various sources, automatically classify it into the right categories and process it in the financial systems.

Example:

A company uses Cognitive RPA to automatically extract and verify invoice data and transfer it to the accounting systems. This leads to a significant reduction in manual working time and minimizes data entry errors.

Detailed information on whether Banks need botsyou will find in the linked article.

Use case - lengthy customer inquiries in customer service

Problem: 

Customer inquiries often require complex interactions and are time-consuming when employees have to manually access extensive databases.

Solution: Cognitive RPA for intelligent customer interaction

A cognitive RPA system understands natural language, analyzes customer data and automatically generates personalized responses.

Example:

In a customer service center, a company uses Cognitive RPA to automatically categorize customer inquiries by email and generate suitable answers from predefined knowledge databases. This leads to faster response times and improved customer service.

Use case - Manual review of contracts and legal documents

Problem:

The manual review of contracts and legal documents is time-consuming and often leads to errors.

Solution: Cognitive RPA for automated contract review

Cognitive RPA automatically analyzes contracts and legal documents, extracts relevant information and checks it for compliance with legal provisions.

Example:

A legal department uses Cognitive RPA to check contracts for specific clauses, deadlines and compliance requirements. This improves the accuracy of the review and speeds up the contract review process.

Use case - High number of errors in IT security checks

Problem:

With manual IT security checks, there is a risk of errors and security gaps.

Solution: Cognitive RPA for automated security checks

Cognitive RPA is able to automatically analyze security protocols, identify vulnerabilities and implement security measures.

Example:

A company uses Cognitive RPA to carry out regular security checks. The system automatically identifies potential vulnerabilities, reports security breaches and can automatically apply security patches if required.

These use cases show how cognitive RPA can be used in different business contexts to increase efficiency, reduce errors and automate complex tasks.

green box with konfuzio logo

Cognitive RPA in document management using the example of Konfuzio

Problem in document management:

In document management, companies are faced with the challenge of efficiently processing large volumes of unstructured documents. 

The manual extraction of relevant information, such as Invoice amounts and data is not only time-consuming, but also error-prone, as human data entry often leads to inaccuracies. This increases the effort required for post-processing and results in potential problems in business processes. 

Cognitive RPA, represented below by Konfuzioan AI-supported software application for intelligent document management, offers a solution here. 

Through advanced cognitive capabilities, such as machine learning, natural language processing and machine vision, Konfuzio enables the automatic processing and extraction of information from documents. 

With this you achieve:

  • A significant increase in efficiency
  • Reduced human error rate
  • Flexibility with changing document structures and formats

For example, Konfuzio significantly reduces the manual workload for automatic invoice processing and enables precise integration into accounting systems.

Concrete solutions through cognitive RPA using the example of Konfuzio

  1. Machine Learning (ML)

    Konfuzio uses machine learning to learn from experience and adapt to new document structures. As a result, workflows are automatically adapted based on acquired knowledge.

  2. Natural Language Processing (NLP)

    The use of natural language processing (NLP) enables Konfuzio to understand and interpret human-like language in documents. This allows interaction with users in natural language and the processing of unstructured text data.

  3. Machine vision

    Konfuzio integrates machine vision to enable automated processing and analysis of images and visual documents.

  4. Decision making

    Cognitive RPA enables Konfuzio to make autonomous decisions based on data and rules, leading to improved problem solving and autonomous action in certain scenarios.

  5. Pattern recognition

    Konfuzio identifies patterns and trends in large document data sets and enables the automatic detection of deviations or anomalies in processes.

  6. Contextual understanding

    With the ability to understand the context of information, Konfuzio considers relevant information and adjusts processing actions accordingly.

  7. Self-optimization

    Konfuzio continuously monitors, analyzes and improves its own document handling processes. This enables automatic adaptation to changing document structures and requirements.

Application example - Automatic invoice processing

Managing the complex tasks associated with payroll accounting requires efficient and precise processing of information. 

Employees are often faced with the challenge of extracting relevant details such as invoice amounts, supplier data and dates from a large number of documents. This time-consuming process is not only costly, but also carries the risk of inconsistencies and resulting problems.

Konfuzio, as a cognitive RPA solution, relies on machine learning to learn from experience and adapt flexibly to different invoice formats. Using natural language processing, Konfuzio interprets human-like language in documents and accurately extracts the relevant information previously defined as rules. In addition, machine vision enables automated processing and analysis of visual documents, including invoices with variable layouts.

The use of Konfuzio in automatic document processing, including invoice processing and payroll accounting, results in a significant increase in efficiency. 

Companies can not only extract precise information, but also react flexibly to adjustments in the invoice structures. This not only reduces the manual workload, but also enables seamless integration of the extracted data into accounting systems for further use. 

Overall, Konfuzio therefore helps to minimize human error, increase efficiency and significantly improve the quality of invoice processing in companies.

Conclusion - business automation through cognitive RPA

In the final analysis, it is clear that cognitive RPA, utilized by advanced solutions such as Konfuzio, is a driving force in the Automation of business processes. 

The integration of machine learning, natural language processing and machine vision opens up new possibilities for the processing and interpretation of unstructured data. 

This technology not only offers a solution for recurring tasks, but also for innovative applications in various business contexts. 

Cognitive RPA also forms the basis for intelligent, adaptive systems that reflect the dynamics of the modern business world. 

With its potential for processing unstructured data and autonomous decision-making, cognitive RPA is undoubtedly at the center of a new era of business automation.

Would you like to fully exploit the possibilities of Cognitive RPA?

Contact us to find out how Konfuzio can be a game-changing solution to help you leverage unstructured data and innovate in business automation. Our experts will contact you immediately to discuss your project in the context of Cognitive RPA.








    "
    "
    Janina Horn Avatar

    Latest articles